SparseCL: Sparse Contrastive Learning for Contradiction Retrieval

Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query, which is important to many downstream applications like fact checking and data cleaning. To retrieve contradiction argument to the query from large document corpora, existing methods such as similarity search and crossencoder models exhibit significant limitations. The former struggles to capture the essence of contradiction due to its inherent nature of favoring similarity, while the latter suffers from computational inefficiency, especially when the size of corpora is large. To address these challenges, we introduce a novel approach: SparseCL that leverages specially trained sentence embeddings designed to preserve subtle, contradictory nuances between sentences. Our method utilizes a combined metric of cosine similarity and a sparsity function to efficiently identify and retrieve documents that contradict a given query. This approach dramatically enhances the speed of contradiction detection by reducing the need for exhaustive document comparisons to simple vector calculations. We validate our model using the Arguana dataset, a benchmark dataset specifically geared towards contradiction retrieval, as well as synthetic contradictions generated from the MSMARCO and HotpotQA datasets using GPT-4. Our experiments demonstrate the efficacy of our approach not only in contradiction retrieval with more than 30% accuracy improvements on MSMARCO and HotpotQA across different model architectures but also in applications such as cleaning corrupted corpora to restore high-quality QA retrieval. This paper outlines a promising direction for improving the accuracy and efficiency of contradiction retrieval in large-scale text corpora

Robustness-Inspired Defense Against Backdoor Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significant threat to their real-world adoption. Despite initial efforts to defend against specific graph backdoor attacks, there is no work on defending against various types of backdoor attacks where generated triggers have different properties. Hence, we first empirically verify that prediction variance under edge dropping is a crucial indicator for identifying poisoned nodes. With this observation, we propose using random edge dropping to detect backdoors and theoretically show that it can efficiently distinguish poisoned nodes from clean ones. Furthermore, we introduce a novel robust training strategy to efficiently counteract the impact of the triggers. Extensive experiments on real-world datasets show that our framework can effectively identify poisoned nodes, significantly degrade the attack success rate, and maintain clean accuracy when defending against various types of graph backdoor attacks with different properties

Learning to Detect Noisy Labels Using Model-Based Features

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption

Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation

The recent advancement of large and powerful models with Text-to-Image (T2I) generation abilities -- such as OpenAI's DALLE-3 and Google's Gemini -- enables users to generate high-quality images from textual prompts. However, it has become increasingly evident that even simple prompts could cause T2I models to exhibit conspicuous social bias in generated images. Such bias might lead to both allocational and representational harms in society, further marginalizing minority groups. Noting this problem, a large body of recent works has been dedicated to investigating different dimensions of bias in T2I systems. However, an extensive review of these studies is lacking, hindering a systematic understanding of current progress and research gaps. We present the first extensive survey on bias in T2I generative models. In this survey, we review prior studies on dimensions of bias: Gender, Skintone, and Geo-Culture. Specifically, we discuss how these works define, evaluate, and mitigate different aspects of bias. We found that: (1) while gender and skintone biases are widely studied, geo-cultural bias remains under-explored; (2) most works on gender and skintone bias investigated occupational association, while other aspects are less frequently studied; (3) almost all gender bias works overlook non-binary identities in their studies; (4) evaluation datasets and metrics are scattered, with no unified framework for measuring biases; and (5) current mitigation methods fail to resolve biases comprehensively. Based on current limitations, we point out future research directions that contribute to human-centric definitions, evaluations, and mitigation of biases. We hope to highlight the importance of studying biases in T2I systems, as well as encourage future efforts to holistically understand and tackle biases, building fair and trustworthy T2I technologies for everyone

The KREEP-rich circum-Lalande region: A candidate landing area for future lunar crewed missions

The lunar magma ocean hypothesis suggests that the primordial KREEP (an acronym of potassium (K), rare earth element (REE), and phosphorus (P)) was the final product of fractional crystallization. However, the primordial KREEP (a.k.a. urKREEP) has never been identified in previous lunar samples or meteorites. The Moon is the focus of many countries’ and agencies’ space exploration plans, and with the advancement of technology, crewed missions have been proposed. We propose two candidate landing sites, located respectively in the northwest (9.5°W, 0.9°S) and southeast (11.1°W, 6.2°S) of Lalande crater (8.6°W, 4.5°S), for future crewed missions, with the primary goal of sampling the speculated urKREEP. Both sites are situated on the Th- (a critical marker of KREEP) and silica-rich Lalande ejecta in the Mare Insularum and Mare Nubium, respectively. Their geolocations at the low latitude on the lunar nearside, the flat surface, and the low rock abundance suggest the sites are safe for landing and meet the needs of real-time Earth–Moon communication. The astronauts could perform many extravehicular activities, such as collecting KREEP-rich samples, screening clast samples, and drilling regolith cores, to gather a variety of samples, such as Lalande ejecta, basalts, Copernicus ejecta, and regolith. The returned samples are valuable to explore the speculated urKREEP, to reveal the relationship between heat-producing elements and volcanism, to refine the lunar cratering chronology function, and to investigate volatiles in the regolith

Elevated first-trimester hepcidin level is associated with reduced risk of iron deficiency anemia in late pregnancy: a prospective cohort study

BackgroundIron deficiency (ID) and iron deficiency anemia (IDA) during pregnancy are highly prevalent worldwide. Hepcidin is considered an important biomarker of iron status. Currently, few longitudinal cohort studies have assessed the potential causal relationship between hepcidin and ID/IDA. Therefore, we aimed to investigate the association of first-trimester maternal serum hepcidin with third-trimester ID/IDA risk in a prospective cohort.MethodsTotal of 353 non-ID/IDA pregnant women at 11–13 weeks’ gestation were enrolled in Southern China and followed up to 38 weeks of gestation. Data on demography and anthropometry were obtained from a structured questionnaire at enrollment. Iron biomarkers including hepcidin were measured at enrollment and follow-up. Regression models were used to evaluate the association of first-trimester hepcidin with third-trimester ID/IDA risk.ResultsSerum hepcidin levels substantially decreased from 19.39 ng/mL in the first trimester to 1.32 ng/mL in the third trimester. Incidences of third-trimester ID and IDA were 46.2 and 11.4%, respectively. Moreover, moderate and high levels of first-trimester hepcidin were positively related to third-trimester hepcidin (log-transformed β = 0.51; 95% CI = 0.01, 1.00 and log-transformed β = 0.66; 95% CI = 0.15, 1.17). Importantly, elevated first-trimester hepcidin was significantly associated with reduced risk of third-trimester IDA (OR = 0.38; 95% CI = 0.15, 0.99), but not with ID after adjustment with potential confounders.ConclusionFirst-trimester hepcidin was negatively associated with IDA risk in late pregnancy, indicating higher first-trimester hepcidin level may predict reduced risk for developing IDA. Nonetheless, given the limited sample size, larger studies are still needed

Clinical and radiological characteristics of pediatric COVID-19 before and after the Omicron outbreak: a multi-center study

IntroductionThe emergence of the Omicron variant has seen changes in the clinical and radiological presentations of COVID-19 in pediatric patients. We sought to compare these features between patients infected in the early phase of the pandemic and those during the Omicron outbreak.MethodsA retrospective study was conducted on 68 pediatric COVID-19 patients, of which 31 were infected with the original SARS-CoV-2 strain (original group) and 37 with the Omicron variant (Omicron group). Clinical symptoms and chest CT scans were examined to assess clinical characteristics, and the extent and severity of lung involvement.ResultsPediatric COVID-19 patients predominantly had normal or mild chest CT findings. The Omicron group demonstrated a significantly reduced CT severity score than the original group. Ground-glass opacities were the prevalent radiological findings in both sets. The Omicron group presented with fewer symptoms, had milder clinical manifestations, and recovered faster than the original group.DiscussionThe clinical and radiological characteristics of pediatric COVID-19 patients have evolved with the advent of the Omicron variant. For children displaying severe symptoms warranting CT examinations, it is crucial to weigh the implications of ionizing radiation and employ customized scanning protocols and protective measures. This research offers insights into the shifting disease spectrum, aiding in the effective diagnosis and treatment of pediatric COVID-19 patients

SIRT1 Overexpression Antagonizes Cellular Senescence with Activated ERK/S6k1 Signaling in Human Diploid Fibroblasts

Sir2, a NAD-dependent deacetylase, modulates lifespan in yeasts, worms and flies. The SIRT1, mammalian homologue of Sir2, regulates signaling for favoring survival in stress. But whether SIRT1 has the function to influence cell viability and senescence under non-stressed conditions in human diploid fibroblasts is far from unknown. Our data showed that enforced SIRT1 expression promoted cell proliferation and antagonized cellular senescence with the characteristic features of delayed Senescence-Associated β-galactosidase (SA-β-gal) staining, reduced Senescence-Associated Heterochromatic Foci (SAHF) formation and G1 phase arrest, increased cell growth rate and extended cellular lifespan in human fibroblasts, while dominant-negative SIRT1 allele (H363Y) did not significantly affect cell growth and senescence but displayed a bit decreased lifespan.. Western blot results showed that SIRT1 reduced the expression of p16INK4A and promoted phosphorylation of Rb. Our data also exposed that overexpression of SIRT1 was accompanied by enhanced activation of ERK and S6K1 signaling. These effects were mimicked in both WI38 cells and 2BS cells by concentration-dependent resveratrol, a SIRT1 activator. It was noted that treatment of SIRT1-.transfected cells with Rapamycin, a mTOR inhibitor, reduced the phosphorylation of S6K1 and the expression of Id1, implying that SIRT1-induced phosphorylation of S6K1 may be partly for the decreased expression of p16INK4A and promoted phosphorylation of Rb in 2BS. It was also observed that the expression of SIRT1 and phosphorylation of ERK and S6K1 was declined in senescent 2BS. These findings suggested that SIRT1-promoted cell proliferation and antagonized cellular senescence in human diploid fibroblasts may be, in part, via the activation of ERK/ S6K1 signaling

Channel Strategies for the Two-Period Closed-Loop Supply Chain with E-Commerce

The aim of this paper is to choose the effective selling channel and reverse channel for a closed-loop supply chain (CLSC) with the e-commerce. The authors formulated six single-selling and dual-selling channel two-period CLSC models in which the manufacturer manufactures new products in the first period and then collects used products by itself, outsourcing to or cooperating with the retailer in the second period. Some interesting and new insights obtained from comparison analysis and numerical experiments are as follows: (1) The leading manufacturer ought to add e-commerce channel, and customers’ e-commerce preference can increase the market demand, collecting rate, and manufacturer’s profit. (2) With the e-commerce channel and the retail channel, dual-collecting channel is the best for the manufacturer and system while the manufacturer collecting channel becomes the best when the collecting competition is relatively large. When the collecting competition exists, retailer collecting channel is the best for the retailer. (3) The market demand, collecting rate, the profits of all members and system will rise by increasing the remanufacturing level and discount coefficient